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Central Prediction System for Time Series Comparison and Analysis of Water Usage Dataopen access

Authors
Ji, MingeunYi, GangmanJung, Jaehee
Issue Date
2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Non-revenue water; time series; ARIMA; additive regression model; water leakage alert system
Citation
IEEE ACCESS, v.8, pp 10342 - 10351
Pages
10
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
8
Start Page
10342
End Page
10351
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/19510
DOI
10.1109/ACCESS.2019.2963373
ISSN
2169-3536
Abstract
Revenue water flow is defined as the amount of water for which the water rate has been collected, against tap water production, whereas non-revenue water (NRW) is defined as water that has been produced, but for which payment cannot be charged. In South Korea, there are big differences in NRW among the regions, and the NRW ratio in urban areas is higher than in rural regions. To reduce regional differences and effectively manage the water system, a management system to lower the NRW ratio is required. In particular, the NRW ratio can be reduced through an automatic leakage detection and sensor-error automatic checking system for feed water pipes and piping in household, and through leakage detection of water supply and drainage pipes that transport large volumes of water. Therefore, this study develops a system that can generate automatic alarms whenever abnormal usage is predicted via analysis of household water flow rate. Linear regression, ARIMA model, and additive regression model are compared to find the best method with high accuracy. The proposed method can support efficient water system management to lower the NRW ratio.
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College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

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